The introduction of targeted therapies and immunotherapy for early-stage lung cancer is associated with improved survival, but patients can only benefit if they partake in adjuvant and neoadjuvant therapies. Data has shown that inequalities exist for patients with lower socioeconomic status as well as non-White patients when it comes to being referred for and receiving treatment after surgery. These inequalities are likely to increase as new drugs are developed in clinical trials comprised of predominantly white patients. In this project, Dr. Nobel will study the impact of disparities on uptake of adjuvant therapy for NSCLC in a largely minority patient population at Montefiore Medical Center in Bronx, NY. She will provide social support and health literacy to engage patients in their care and collect genetic data about their tumors, which will contribute to future clinical trials that are more inclusive.
- Research Summary
Systemic therapy after surgery to remove lung cancer has been demonstrated to improve survival. However, data has shown that there are inequalities in which patients are referred for and receive treatment after surgery, specifically for lower socioeconomic status and non-White patients. As new treatments have been developed, these inequalities are likely to increase as these drugs have been developed in clinical trials predominantly composed of White patients and the benefits in other populations are not known. We have previously demonstrated that using nursing and peer navigators to help guide patients in their cancer care improves treatment adherence in our predominantly Black and Hispanic low socioeconomic status population in the Bronx. The BRONx-TEAM project aims to improve patient outcomes by using a navigation pathway focused on increasing patient adherence to systemic therapy after surgery for non-small cell lung cancer resection. We believe that by providing social support and improving health literacy we can get patients to be more informed and engaged in their cancer care. Furthermore, we will gather genetic data about the patients tumors. Given our patient population, we have a unique opportunity to contribute to the literature to understand the relationships between tumor genetics, treatment types and outcomes in non-White patients. Furthermore, we will investigate the use of a commercial genetic panel to assess risk for recurrence. Given the lack of this type of data in low income non-White patients, we believe that this exploratory portion of our study will serve as an important foundation for future clinical trials that are more inclusive than the currently available literature.
- Technical Abstract
As seen in the phase III trial CheckMate 816 (CM816), neoadjuvant anti-PD-1+chemotherapy improves survival for patients with resectable non-small cell lung cancer (NSCLC), with pathologic response as a major trial endpoint. Our team led the Central Pathology Review for CM816, and we showed the first prospective evidence that the full spectrum of % residual viable tumor (%RVT) associates with event free survival. Given the data supporting pathologic response as a survival surrogate, %RVT will likely be incorporated into the next generation of clinical trials and may ultimately guide clinical decision-making. %RVT is primarily evaluated using visual assessment of routine hematoxylin and eosin-stained slides. We developed a machine learning-based approach to score %RVT, which allows for a standardized approach that can be completed rapidly for a large volume of patients, and we propose to test this algorithm in resection specimens from CM816. Additionally, we will use multiplex immunofluorescence (mIF) to quantify individual features of pathologic response, locate them within the larger tumor bed, and determine the relative contribution in predicting patient outcomes. Furthermore, we will use the novel AstroPath platform, a mIF whole-slide imaging platform that uses algorithms first developed in astronomy to generate tumor-immune maps, to identify additional pre- and on-treatment biomarkers of response. Our goal is to leverage emerging technologies (i.e, machine learning and mIF) to develop the next generation of pathology biomarkers, including pathologic response assessment, and to identify additional features that can potentially be targeted in combination with anti-PD-(L)1+chemotherapy to improve clinical benefit in patients with NSCLC.